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You can still evaluate an AI product when its vendor will not reveal the model or full implementation, but you cannot treat silence as proof of quality. Separate every claim from evidence that applies to the exact product, model or system version, deployment configuration, intended use and date. Map the complete system boundary, test performance and failure modes in your context, examine data and security controls, and make approval conditional on controls for what remains unknown. A missing model card, benchmark detail or audit report should narrow the permitted use, increase human oversight, trigger a limited pilot or stop the purchase—not receive an assumed passing score.
Why does limited transparency change an AI purchasing decision?
Opacity increases the uncertainty around both benefit and harm. A polished policy, framework logo, benchmark slide or compliance badge may describe a company or a base model without proving that the product you will operate is accurate, secure, fair or suitable for your users. The AI TrustMark guide, updated 19 September 2026, puts the principle plainly: “Marketing claims alone are not verified evidence.”
The evidence burden should track the consequences of an error. A drafting assistant that a trained employee edits before publication is different from a system that ranks applicants, approves credit, denies benefits, diagnoses a patient or takes an irreversible action. Define the decision, affected people, unacceptable failure modes and escalation route before accepting a vendor’s assurances. For high-impact uses, require stronger evidence and tighter operational controls than for low-impact experimentation.
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How do I define the decision and harm boundary?
Write a one-page use statement before requesting documents. It should say what the system will do, what it will never do, who can be affected, and who remains accountable for the outcome.
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- Intended task: describe the input, output and business decision in plain language.
- Population and setting: identify users, affected groups, languages, locations, accessibility needs and operating environments.
- Failure tolerance: set required accuracy or reliability, unacceptable false positives and false negatives, latency limits and availability needs.
- Human role: name the reviewer, required training, override authority and escalation path.
- Irreversibility: mark actions that affect rights, money, safety, employment, health or access to services and therefore need the highest scrutiny.
- Jurisdiction: list the countries, sector rules and contractual obligations that apply; legal duties vary by location and use case.
This statement prevents a vendor from answering a broad question such as “Is the model safe?” with a result that applies only to a different task or population.
What exactly is the AI system I am evaluating?
Evaluate the deployed system, not just the name of a foundation model. Record each component and the way it can change.
| System element | Questions to document |
|---|---|
| Model and version | What model, release, fine-tune and safety policy produce the output? Is the version fixed, and can the vendor switch it without approval? |
| Prompt and orchestration | Which system prompts, routing rules, ranking logic, agents or human-review queues alter the model’s response? |
| Retrieval and data stores | Which indexes, knowledge bases, customer files and freshness controls supply context? How are access permissions enforced? |
| Safety and moderation | Which filters, classifiers, refusal rules and post-processing layers run before or after generation? |
| Hosting and infrastructure | Where is processing performed, which cloud or region is used, and what happens during an outage or capacity shortage? |
| External services | Which APIs, subprocessors, telemetry services and third-party models receive data or influence results? |
| Interfaces and actions | Can the system send messages, change records, call tools or make decisions automatically? What permissions and approvals limit it? |
| Update channel | How are model, dependency, policy and data changes announced, tested, rolled back and logged? |
Ask what can change without customer approval and which failure could interrupt service, expose data or alter risk. NIST governance guidance treats procurement, cybersecurity, computational infrastructure, data, deployment mechanics and system failure as connected concerns, rather than separate checkboxes.
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Send a structured request and insist that each answer identifies the product version, deployment configuration, date and limitations. A useful minimum disclosure pack includes:
- Intended and out-of-scope uses, known failure modes, operating limits and user instructions.
- A model card or system card for the exact version; a generic card for a base model is not enough.
- Training and evaluation data descriptions, including provenance, licensing, collection method and representativeness.
- Task-specific performance by relevant population, language, environment and operating condition, with confidence intervals or other uncertainty information where available.
- Test sets, metrics, validation methods, red-team or adversarial testing, tools, processes and test dates.
- Privacy and data-flow documentation: purpose, processing location, retention, deletion, access, subprocessors, customer-content use, training or improvement use and tenant separation.
- Security architecture, identity and access controls, encryption, vulnerability handling, incident response, logging and independent assurance.
- Human-oversight design, override procedures or rates, user training, support, complaints and redress channels.
- Update, rollback, deprecation and material-change notification terms.
- Audit rights, indemnity scope, service levels, business-continuity commitments and exit or portability provisions.
The NIST procurement workbook asks suppliers whether they permit independent third-party audits and can provide artifacts such as test reports, logs and quality criteria. A refusal is therefore a procurement fact to record, not a reason to lower the standard.
How should I use model cards, system cards and datasheets?
These artifacts organize evidence; they do not guarantee performance. A model card should state a model’s purpose, intended and off-label uses, performance, evaluation data and limitations. A datasheet describes a dataset’s motivation, composition, collection and recommended uses. A system card addresses the complete system and deployment context, including components that a base-model card cannot see.
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Check each document for:
- Exact model or system identifier, release date and version history.
- Evaluation population, languages, environments, test-set construction and metrics.
- Known limitations, excluded uses, failure examples and uncertainty.
- Whether the evaluation covers retrieval, prompts, moderation, ranking, tools and human review used in your purchased configuration.
- Ownership and update date, plus a way to obtain revisions when the product changes.
A missing version, date, test population or limitation section is an evidence gap. It does not prove the vendor performed no testing, but it means you cannot verify the claim from the supplied material. Ask for a confidential review, redacted summary or customer-controlled test rather than inferring a positive result.
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Turn sales statements into auditable rows. For every material claim, capture:
- Claim: quote or precisely paraphrase what the vendor promises.
- Evidence: identify the document, test, contract term or independent assessment.
- Scope: record product, model or system version, region, configuration, user population and intended task.
- Method: note test data, metrics, tools, process, sample size and date.
- Verification: mark whether an independent party reproduced or reviewed it.
- Limitations: record exclusions, uncertainty, unresolved defects and conditions that differ from your deployment.
- Decision impact: state which control, use restriction or approval condition follows.
Use “not stated” when the vendor supplies no answer. The absence of a document is not evidence that a control exists. Keep the register with procurement records so a later model update can be compared with the original decision.
How do I test whether a vendor’s performance claim applies to me?
Benchmark numbers are meaningful only when the tested task, data, metric, model version and operating conditions match your use. Ask who ran the test, whether the vendor selected the data, how cases were sampled, what baseline was used and whether the result was measured before or after human review.
Require evidence for ordinary, edge, adversarial and degraded conditions. For example, test incomplete inputs, ambiguous language, rare cases, prompt injection, stale retrieval documents, permissions errors, service interruption and a sudden traffic increase. Measure not only average quality but also harmful errors, abstention or escalation behavior, latency, availability and subgroup outcomes relevant to your users.
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How should I compare two AI products fairly?
Use a weighted scorecard designed for the use case, and do not combine incomparable benchmark figures into a single “best AI” ranking. NIST identifies validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed as trustworthiness characteristics.
| Evaluation axis | Questions to answer | Possible decision evidence |
|---|---|---|
| Purpose and fit | Is the proposed use inside the documented scope? | Use statement, system card, limitation list |
| Validity and reliability | Are task results repeatable under our data and workflow? | Reproducible test, error analysis, versioned metrics |
| Safety and robustness | What happens with unusual, adversarial or degraded inputs? | Red-team results, abuse tests, safe-fail behavior |
| Fairness | Are relevant groups measured, and are harms mitigated? | Disaggregated results, mitigation and escalation plan |
| Explainability | Can operators and affected people understand and challenge outputs? | Reason codes, provenance, appeal and correction process |
| Privacy and data governance | What is collected, retained, shared or used for training? | Data-flow map, retention schedule, contractual restrictions |
| Security and resilience | How are access, isolation, vulnerabilities, incidents and outages handled? | Architecture, assurance report, logs, recovery tests |
| Accountability | Are owners, audit rights and redress routes defined? | Named owner, audit clause, complaint procedure |
| Change control | Are model, dependency and policy changes disclosed and reversible? | Notice period, acceptance test, rollback and exit terms |
| Operational fit | Can staff use, monitor, override and safely stop the system? | Training, runbook, staffing and stop criteria |
Set weights before seeing results, document why each weight reflects harm or business dependence, and keep “unknown” separate from “failed.” An unverified capability should not receive the same score as a tested capability.
What privacy and security questions are easy to miss?
Ask where every input, output, log, embedding and support ticket goes. Confirm whether customer content is retained, used to train or improve a shared model, visible to human reviewers, or separated from other tenants. Require deletion timelines, legal hold handling, export format and proof of deletion where appropriate.
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For security, examine identity and role-based access, encryption in transit and at rest, secrets management, network isolation, software and model supply-chain controls, vulnerability disclosure, penetration testing, incident notification, log retention and recovery objectives. Test that a user cannot retrieve another tenant’s documents through search, prompts or tool calls. A vendor’s general security certification may cover its hosting environment while omitting the AI-specific retrieval, prompt or output risks you need to control.
What should the contract require when documentation is incomplete?
Convert material uncertainty into enforceable terms. Depending on risk, seek:
- Notice and customer approval for material model, data, subprocessor or policy changes.
- Advance access to release notes, test summaries and a defined acceptance test.
- Rollback, suspension and termination rights when performance, privacy or safety thresholds are missed.
- Audit cooperation, access to logs and preservation of evidence after an incident.
- Clear ownership of outputs, confidentiality, data-use limits, deletion and portability.
- Service levels, support response times, outage communications and recovery commitments.
- Indemnity language that matches the actual risks and does not quietly exclude the vendor’s AI-specific obligations.
Legal and regulatory requirements depend on jurisdiction and use case, so have counsel translate the risk register into terms rather than relying on a generic “responsible AI” statement.
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What should I do when a vendor refuses to answer?
Record the exact question, the vendor’s stated reason, the risk created and any narrower claim that can be verified. Distinguish legitimate protection of trade secrets from refusal to provide evidence about outcomes, data handling or operational controls. You do not need source code to require test conditions, auditability, incident reporting and limits on use.
Offer proportionate alternatives:
- Confidential review under a non-disclosure agreement.
- An independent assurance report or redacted test summary.
- Customer-controlled evaluation using representative, privacy-safe data.
- Contractual commitments, audit rights and change notification.
- A limited pilot with human review, restricted permissions, logging and a stop trigger.
If the remaining uncertainty could cause material harm, defer or reject the purchase. Do not award a high score because a vendor has a polished policy or refuses on the ground that “the model is proprietary.”
How do I run a safe pilot before full deployment?
A pilot should answer a decision question, not serve as an informal product demonstration. Define success and stop criteria in advance, use representative cases without unnecessary personal data, and compare the AI with the current human or software baseline.
- Limit users, data fields, permissions and external actions.
- Require a trained human to review every consequential output.
- Log prompts, retrieved sources, outputs, overrides, latency, errors and incidents.
- Sample ordinary, rare, adversarial and out-of-scope cases; record subgroup results where relevant.
- Test outage, rollback, deletion and support procedures, not only answer quality.
- Review findings with technical, security, privacy, legal, domain and affected-user representatives.
- Approve only the documented configuration and use; reopen the decision if scope or version changes.
What controls are needed after purchase?
Transparency is a lifecycle obligation. Assign an accountable owner and maintain a record of the approved model, system boundary, data flows, evidence and restrictions. Monitor:
- Performance and drift against the pilot baseline.
- Incidents, near misses, unsafe outputs and user complaints.
- Override, abstention and escalation rates.
- Subgroup outcomes and emerging fairness concerns.
- Access logs, retrieval failures, prompt-injection attempts and unusual tool use.
- Vendor releases, dependency changes, deprecations and assurance-report updates.
Define thresholds that trigger investigation, human-only operation, rollback, customer notification or termination. Test again after every material update rather than assuming a new version is equivalent to the approved one. NIST’s accountable-and-transparent characteristic covers design decisions, training data, model training, intended uses, deployment decisions and who made those decisions; preserve that chain so an affected person or auditor can understand how the system was governed.
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Use a written decision that states what is known, what is unknown and how the uncertainty is controlled. Approve only when the evidence matches the exact product and use, residual risks are within the organization’s tolerance, and an owner can monitor and stop the system.
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- Approve: evidence is current and relevant, controls are contractually enforceable, and monitoring and rollback are ready.
- Approve with restrictions: allow a narrower task, smaller population, lower permissions or mandatory human review while specified evidence gaps remain.
- Pilot only: uncertainty is material but can be bounded by time, data, access, logging and stop criteria.
- Defer: ask for missing evidence or contract changes before handling real users or sensitive data.
- Reject: the vendor cannot verify a risk that is unacceptable, or cannot provide safe operation, oversight or exit.
The NIST AI Risk Management Framework, released 26 January 2023, is voluntary and intended to help organizations incorporate trustworthiness into the design, development, use and evaluation of AI products, services and systems. It is a method for organizing your decision, not a universal vendor ranking or a substitute for evidence.
What are the most common evaluation mistakes?
- Scoring a marketing promise as if it were a test result.
- Reviewing a foundation-model card while ignoring retrieval, prompts, moderation, tools and human-review layers.
- Comparing benchmark numbers from different versions, datasets, metrics or dates.
- Treating silence as proof that a control exists.
- Testing average accuracy while ignoring harmful edge cases, subgroup outcomes, latency and outages.
- Buying without a change-notification, audit, rollback or exit mechanism.
- Allowing a pilot to expand into production without a new approval decision.
What does a defensible evaluation record contain?
Keep the use statement, system-boundary diagram, claim-to-evidence register, disclosure request and responses, test plan and results, risk and fairness analysis, privacy and security review, contract controls, pilot logs, approval decision, named owner and monitoring plan together. Date every artifact and identify the exact version and configuration. This record lets you explain why the product was accepted, what limits apply and what evidence would require a pause or reversal.
Frequently Asked Questions
Can I trust an AI product that has no model card?
You can use it only if other evidence covers the same functions: a system description, contextual testing, data-flow and security documentation, operating limits, human oversight and enforceable change controls. For a consequential use, the missing card should normally lead to a restricted pilot or a purchasing pause, not an automatic approval.
How much transparency is enough for a low-risk AI tool?
The answer depends on the harm boundary, not on whether the tool is marketed as low risk. At minimum, verify what data leaves your environment, whether content is retained or reused, who can access outputs, how users report errors and how you can disable the service; increase evidence and oversight if the tool starts influencing people or taking actions.
Will signing an NDA solve a vendor’s lack of transparency?
An NDA may enable a confidential review of test summaries, architecture or subprocessors, but it does not create evidence or a right to audit. Pair confidentiality terms with dated deliverables, acceptance tests, incident notice, change approval, deletion commitments and a practical exit route.
How can I normalize two vendors’ benchmark claims?
Create a common test set and protocol, run both products in the configurations you would buy, and record model version, prompts, retrieval sources, metrics, sampling and date. If a vendor will not support comparable testing, label its published number as non-comparable and keep it out of the weighted performance score.
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